AI agents and chatbots for business

A business chatbot or AI agent only delivers value when it is connected to the actual data and systems of the business. AC develops custom AI agents and chatbots: assistants that answer from your documents and data, and agents that take actions inside your systems, with permissions, human approval for sensitive steps and an audit trail of every action.

Chatbot vs. AI agent

• Chatbot and assistant: answers customer or employee questions from the information you define, such as procedures, catalog, documents and system data. A good fit for customer service, internal support and fast search across company knowledge.
• AI agent: understands a free-text request and carries it out in the system, such as checking an order status, opening a ticket, updating a customer record, drafting a quote or triggering a workflow. A good fit when much of the work is turning requests into system actions.

What we build

• Chatbots grounded in your business data: semantic search across documents and data, answers with sources, and hand-off to a person when needed.
• AI assistants inside existing systems: a chat panel in your CRM, management system or customer portal that is aware of the current screen and user.
• AI agents that take actions: a defined, limited set of actions with per-user permissions and human approval for sensitive steps.
• Agents connected to your systems: integration with ERP, CRM and other systems through APIs, so the agent works on up-to-date data.
• Monitoring and measurement: every conversation and action logged, tracking of answer quality and usage cost, and tools to correct and improve.

From our experience

AC's founder, Adir Cohen, leads a B2B platform that can be fully operated through AI: business processes, workflows, interfaces and integrations are configured through conversation rather than through settings screens. The same principles define a good AI agent: defined actions, clear permissions and full logging.

How to start

1. Pick one use case: for example, answering customer questions or handling a frequent internal request.
2. Define the data and actions: what the agent answers from, what it may do and what needs approval.
3. Build a first version and test it against real questions and requests.
4. Measure and expand: quality, time saved and cost, then add data sources and actions.

Next step

AI agents and chatbots are part of our AI development and integration work and usually rely on integrations with your existing systems. Have an agent or chatbot in mind? Let's talk about it.

Frequently asked questions

What is the difference between a chatbot and an AI agent?

A chatbot answers questions from information it was given. An AI agent also takes actions: it updates a record, opens an order, sends a document or triggers a workflow, within the permissions it was granted and with every action logged. Many projects start as a chatbot and evolve into an AI agent.

What information does the chatbot answer from?

From the information you choose: documents, procedures, product catalog, FAQs and data from your systems. The chatbot searches that information, drafts an answer and cites its source. When the answer is not in the data, it says so or hands the conversation over to a person.

How do you stop an AI agent from making mistakes in the system?

The agent only works through predefined actions and never gets unrestricted access to the system. Every action is limited to the user's permissions, sensitive actions require human approval, and every action is logged so it can be reviewed and reversed. We start with a small set of actions and expand once they prove reliable.

Is our data safe?

We define up front which data is sent to the language model, and each user only sees what they are permitted to. Conversations and actions are logged, and usage limits can be set per user or organization. The provider and terms of use are chosen to match the sensitivity of the data.

How much does an AI agent or chatbot cost to build?

It depends on the number of data sources, how many actions the agent performs, the integrations with your existing systems and the channels it is available on (website, internal system or messaging). There is also an ongoing language model usage cost. After a short discovery phase we provide a detailed estimate and usually recommend starting with a focused first version.